Assessing methods for assigning SNPs to genes in gene-based tests of association using common variants

Elucidation of novel SNPs affecting immune response to classical swine fever vaccination in pigs using immunogenomics approach

The host genetic makeup plays a significant role in causing the within-breed variation among individuals after vaccination. The present study was undertaken to elucidate the genetic basis of differential immune response between high and low responder Landlly (Landrace X Ghurrah) piglets vis-à-vis CSF vaccination. For the purpose, E2 antibody response against CSF vaccination was estimated in sampled animals on the day of vaccination and 21-day post-vaccination as a measure of humoral immune response. Double-digestion restriction associated DNA (ddRAD) sequencing was undertaken on 96 randomly chosen Landlly piglets using Illumina HiSeq platform. SNP markers were called using standard methodology. Genome-wide association study (GWAS) was undertaken in PLINK program to identify the informative SNP markers significantly associated with differential immune response. The results revealed significant SNPs associated with E2 antibody response against CSF vaccination. The genome-wide informative SNPs for the humoral immune response against CSF vaccination were located on SSC10, SSC17, SSC9, SSC2, SSC3 and SSC6. The overlapping and flanking genes (500Kb upstream and downstream) of significant SNPs were CYB5R1, PCMTD2, WT1, IL9R, CD101, TMEM64, TLR6, PIGG, ADIPOR1, PRSS37, EIF3M, and DNAJC24. Functional enrichment and annotation analysis were undertaken for these genes in order to gain maximum insights into the association of these genes with immune system functionality in pigs. The genetic makeup was associated with differential immune response against CSF vaccination in Landlly piglets while the identified informative SNPs may be used as suitable markers for determining variation in host immune response against CSF vaccination in pigs.

A gene based combination test using GWAS summary data

BACKGROUND

Gene-based association tests provide a useful alternative and complement to the usual single marker association tests, especially in genome-wide association studies (GWAS). The way of weighting for variants in a gene plays an important role in boosting the power of a gene-based association test. Appropriate weights can boost statistical power, especially when detecting genetic variants with weak effects on a trait. One major limitation of existing gene-based association tests lies in using weights that are predetermined biologically or empirically. This limitation often attenuates the power of a test. On another hand, effect sizes or directions of causal genetic variants in real data are usually unknown, driving a need for a flexible yet robust methodology of gene based association tests. Furthermore, access to individual-level data is often limited, while thousands of GWAS summary data are publicly and freely available.

RESULTS

To resolve these limitations, we propose a combination test named as OWC which is based on summary statistics from GWAS data. Several traditional methods including burden test, weighted sum of squared score test [SSU], weighted sum statistic [WSS], SNP-set Kernel Association Test [SKAT], and the score test are special cases of OWC. To evaluate the performance of OWC, we perform extensive simulation studies. Results of simulation studies demonstrate that OWC outperforms several existing popular methods. We further show that OWC outperforms comparison methods in real-world data analyses using schizophrenia GWAS summary data and a fasting glucose GWAS meta-analysis data. The proposed method is implemented in an R package available at https://github.com/Xuexia-Wang/OWC-R-package CONCLUSIONS: We propose a novel gene-based association test that incorporates four different weighting schemes (two constant weights and two weights proportional to normal statistic Z) and includes several popular methods as its special cases. Results of the simulation studies and real data analyses illustrate that the proposed test, OWC, outperforms comparable methods in most scenarios. These results demonstrate that OWC is a useful tool that adapts to the underlying biological model for a disease by weighting appropriately genetic variants and combination of well-known gene-based tests.

A Bayesian Hierarchical Framework for Pathway Analysis in Genome-Wide Association Studies

BACKGROUND

Pathway analysis allows joint consideration of multiple SNPs belonging to multiple genes, which in turn belong to a biologically defined pathway. This type of analysis is usually more powerful than single-SNP analyses for detecting joint effects of variants in a pathway.

METHODS

We develop a Bayesian hierarchical model by fully modeling the 3-level hierarchy, namely, SNP-gene-pathway that is naturally inherent in the structure of the pathways, unlike the currently used ad hoc ways of combining such information. We model the effects at each level conditional on the effects of the levels preceding them within the generalized linear model framework. To deal with the high dimensionality, we regularize the regression coefficients through an appropriate choice of priors. The model is fit using a combination of iteratively weighted least squares and expectation-maximization algorithms to estimate the posterior modes and their standard errors. A normal approximation is used for inference.

RESULTS

We conduct simulations to study the proposed method and find that our method has higher power than some standard approaches in several settings for identifying pathways with multiple modest-sized variants. We illustrate the method by analyzing data from two genome-wide association studies on breast and renal cancers.

CONCLUSION

Our method can be helpful in detecting pathway association.

An Exome-Wide Association Study Identifies New Susceptibility Loci for Age of Smoking Initiation in African- and European-American Populations

INTRODUCTION

Cigarette smoking is one of the largest causes of preventable death worldwide. This study aimed to identify susceptibility loci for age at smoking initiation (ASI) by performing an exome-wide association analysis.

METHODS

A total of 2510 smokers of either African-American (AA) or European-American (EA) origin were genotyped and analyzed at both the single nucleotide polymorphism (SNP) and gene levels. After removal of those SNPs with a minor allele frequency (<0.01), 48091 and 34933 SNPs for AAs and EAs, respectively, were used to conduct a SNP-based association analysis. Gene-based analyses were then performed for all SNPs examined within each gene. Further, we estimated the proportion of variance explained by all common SNPs included in the analysis.

RESULTS

The strongest signals were detected for SNPs rs17849904 in the pitrilysin metallopeptidase 1 gene (PITRM1) in the AA sample (p = 9.02 × 10-7) and rs34722354 in the discoidin domain of the receptor tyrosine kinase 2 gene (DDR2) in the EA sample (p = 9.74 × 10-7). Both SNPs remained significant after Bonferroni correction for the number of SNPs tested. Subsequently, the gene-based association analysis revealed a significantly associated gene, DHRS7, in the AA sample (p = 5.00 × 10-6), a gene previously implicated in nicotine metabolism.

CONCLUSIONS

Our study revealed two susceptibility loci for age of smoking initiation in the two ethnic samples, with the first being PITRM1 for AA smokers and the second DDR2 for EA smokers. In addition, we found DHRS7 to be a plausible candidate for ASI in the AA sample from our gene-based association analysis.

IMPLICATIONS

PITRM1 and DHRS7 for African-American smokers and DDR2 for European-American smokers are new candidate genes for smoking initiation. These genes represent new additions to smoking initiation, an important but less studied phenotype in nicotine dependence research.

A network analysis to identify mediators of germline-driven differences in breast cancer prognosis

Identifying the underlying genetic drivers of the heritability of breast cancer prognosis remains elusive. We adapt a network-based approach to handle underpowered complex datasets to provide new insights into the potential function of germline variants in breast cancer prognosis. This network-based analysis studies ~7.3 million variants in 84,457 breast cancer patients in relation to breast cancer survival and confirms the results on 12,381 independent patients. Aggregating the prognostic effects of genetic variants across multiple genes, we identify four gene modules associated with survival in estrogen receptor (ER)-negative and one in ER-positive disease. The modules show biological enrichment for cancer-related processes such as G-alpha signaling, circadian clock, angiogenesis, and Rho-GTPases in apoptosis.

A powerful conditional gene-based association approach implicated functionally important genes for schizophrenia

MOTIVATION

It remains challenging to unravel new susceptibility genes of complex diseases and the mechanisms in genome-wide association studies. There are at least two difficulties, isolation of the genuine susceptibility genes from many indirectly associated genes and functional validation of these genes.

RESULTS

We first proposed a novel conditional gene-based association test which can use only summary statistics to isolate independently associated genes of a disease. Applying this method, we detected 185 genes of independent association with schizophrenia. We then designed an in-silico experiment based on expression/co-expression to systematically validate pathogenic potential of these genes. We found that genes of independent association with schizophrenia formed more co-expression pairs in normal post-natal but not pre-natal human brain regions than expected. Interestingly, no co-expression enrichment was found in the brain regions of schizophrenia patients. The genes with independent association also had more significant P-values for differential expression between schizophrenia patients and controls in the brain regions. In contrast, indirectly associated genes or associated genes by other widely-used gene-based tests had no such differential expression and co-expression patterns. In summary, this conditional gene-based association test is effective for isolating directly associated genes from indirectly associated genes, and the results insightfully suggest that common variants might contribute to schizophrenia largely by distorting expression and co-expression in post-natal brains.

AVAILABILITY AND IMPLEMENTATION

The conditional gene-based association test has been implemented in a platform 'KGG' in Java and is publicly available at http://grass.cgs.hku.hk/limx/kgg/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Precision health: a primer for physiotherapists

Health care provision is changing, and so is the information we use to guide decisions related to patient care. Increasingly, health practitioners will need to deal with genetics and 'big data' in the context of clinical practice. Indeed, commercial packages for consumer genetic testing are already widely available, and devices enabling self-monitoring of health are in daily use by many of our patients. "Precision health" (distinct from "precision medicine") provides a model, which allows us to bring our genome together with our external environment (lifestyles, societal influences etc.) and eventually, our transient internal environment (reflected by 'omics'), to optimise disease prevention and care. Such advancements have given rise to a need for primary health care clinicians to understand basic genetic and precision health concepts. This editorial meets this need, serving as a primer by providing the following: an introduction to current primary health challenges; description of the key elements of the precision health model; an overview of basic genetic, and associated research concepts; a snapshot of some clinically pertinent research in the context of precision health; and a brief discussion of challenges and future directions.

ARMC 5 Variants and Risk of Hypertension in Blacks: MH- GRID Study

Background We recently found that ARMC 5 variants may be associated with primary aldosteronism in blacks. We investigated a cohort from the MH - GRID (Minority Health Genomics and Translational Research Bio-Repository Database) and tested the association between ARMC 5 variants and blood pressure in black s. Methods and Results Whole exome sequencing data of 1377 black s were analyzed. Target single-variant and gene-based association analyses of hypertension were performed for ARMC 5, and replicated in a subset of 3015 individuals of African descent from the UK Biobank cohort. Sixteen rare variants were significantly associated with hypertension ( P=0.0402) in the gene-based (optimized sequenced kernel association test) analysis; the 16 and one other, rs116201073, together, showed a strong association ( P=0.0003) with blood pressure in this data set. The presence of the rs116201073 variant was associated with lower blood pressure. We then used human embryonic kidney 293 and adrenocortical H295R cells transfected with an ARMC 5 construct containing rs116201073 (c.*920T>C). The latter was common in both the discovery ( MH - GRID ) and replication ( UK Biobank) data and reached statistical significance ( P=0.044 [odds ratio, 0.7] and P=0.007 [odds ratio, 0.76], respectively). The allele carrying rs116201073 increased levels of ARMC5 mRNA , consistent with its protective effect in the epidemiological data. Conclusions ARMC 5 shows an association with hypertension in black s when rare variants within the gene are considered. We also identified a protective variant of the ARMC 5 gene with an effect on ARMC 5 expression confirmed in vitro. These results extend our previous report of ARMC 5's possible involvement in the determination of blood pressure in blacks.

A statistical approach to fine-mapping for the identification of potential causal variants related to human intelligence

Genome-wide association studies (GWASs) have identified >20 genetic loci associated with human intelligence. However, due to correlations between the trait-associated SNPs, only a few of the loci are confirmed to have a true biological effect. In order to distinguish the SNPs that have a causal effect on human intelligence, we must eliminate the noise from the high degree of linkage disequilibrium that persists throughout the genome. In this study, we apply a novel PAINTOR fine-mapping method, which uses a Bayesian approach to determine the SNPs with the highest probability of causality. This technique incorporates the GWAS summary statistics, linkage disequilibrium structure, and functional annotations to compute the posterior probability of causality for all SNPs in the GWAS-associated regions. We found five SNPs (rs6002620, rs41352752, rs6568547, rs138592330, and rs28371699) with a high probability of causality, three of which have posterior probabilities >0.60. The SNP rs6002620 (NDUFA6), which is involved in mitochondrial function, has the highest likelihood of causality. These findings provide important insight into the genetic determinants contributing to human intelligence.

Shaping the nebulous enhancer in the era of high-throughput assays and genome editing

Since the 1st discovery of transcriptional enhancers in 1981, their textbook definition has remained largely unchanged in the past 37 years. With the emergence of high-throughput assays and genome editing, which are switching the paradigm from bottom-up discovery and testing of individual enhancers to top-down profiling of enhancer activities genome-wide, it has become increasingly evidenced that this classical definition has left substantial gray areas in different aspects. Here we survey a representative set of recent research articles and report the definitions of enhancers they have adopted. The results reveal that a wide spectrum of definitions is used usually without the definition stated explicitly, which could lead to difficulties in data interpretation and downstream analyses. Based on these findings, we discuss the practical implications and suggestions for future studies.

Learning the optimal scale for GWAS through hierarchical SNP aggregation

Background

Genome-Wide Association Studies (GWAS) seek to identify causal genomic variants associated with rare human diseases. The classical statistical approach for detecting these variants is based on univariate hypothesis testing, with healthy individuals being tested against affected individuals at each locus. Given that an individual’s genotype is characterized by up to one million SNPs, this approach lacks precision, since it may yield a large number of false positives that can lead to erroneous conclusions about genetic associations with the disease. One way to improve the detection of true genetic associations is to reduce the number of hypotheses to be tested by grouping SNPs.

Results

We propose a dimension-reduction approach which can be applied in the context of GWAS by making use of the haplotype structure of the human genome. We compare our method with standard univariate and group-based approaches on both synthetic and real GWAS data.

Conclusion

We show that reducing the dimension of the predictor matrix by aggregating SNPs gives a greater precision in the detection of associations between the phenotype and genomic regions.

Epigenetic dysregulation of naive CD4+ T-cell activation genes in childhood food allergy

Food allergy poses a significant clinical and public health burden affecting 2–10% of infants. Using integrated DNA methylation and transcriptomic profiling, we found that polyclonal activation of naive CD4+ T cells through the T cell receptor results in poorer lymphoproliferative responses in children with immunoglobulin E (IgE)-mediated food allergy. Reduced expression of cell cycle-related targets of the E2F and MYC transcription factor networks, and remodeling of DNA methylation at metabolic (RPTOR, PIK3D, MAPK1, FOXO1) and inflammatory genes (IL1R, IL18RAP, CD82) underpins this suboptimal response. Infants who fail to resolve food allergy in later childhood exhibit cumulative increases in epigenetic disruption at T cell activation genes and poorer lymphoproliferative responses compared to children who resolved food allergy. Our data indicate epigenetic dysregulation in the early stages of signal transduction through the T cell receptor complex, and likely reflects pathways modified by gene–environment interactions in food allergy.

Immunoglobulin E (IgE)-mediated food allergy is a major issue that affects 2–10% of infants. Here the authors study the epigenetic regulation of the naive CD4+ T cell activation response among children with IgE-mediated food allergy finding epigenetic dysregulation in the early stages of signal transduction through the T cell receptor complex.

PR Interval Associated Genes, Atrial Remodeling and Rhythm Outcome of Catheter Ablation of Atrial Fibrillation-A Gene-Based Analysis of GWAS Data

Background: PR interval prolongation has recently been shown to associate with advanced left atrial remodeling and atrial fibrillation (AF) recurrence after catheter ablation. While different genome-wide association studies (GWAS) have implicated 13 loci to associate with the PR interval as an AF endophenotype their subsequent associations with AF remodeling and response to catheter ablation are unknown. Here, we perform a gene-based analysis of GWAS data to test the hypothesis that PR interval candidate genes also associate with left atrial remodeling and arrhythmia recurrence following AF catheter ablation.

Methods and Results: Samples from 660 patients with paroxysmal (n = 370) or persistent AF (n = 290) undergoing AF catheter ablation were genotyped for ~1,000,000 SNPs. Gene-based association was investigated using VEGAS (versatile gene-based association study). Among the 13 candidate genes, SLC8A1, MEIS1, ITGA9, SCN5A, and SOX5 associated with the PR interval. Of those, ITGA9 and SOX5 were significantly associated with left atrial low voltage areas and left atrial diameter and subsequently with AF recurrence after radiofrequency catheter ablation.

Conclusion: This study suggests contributions of ITGA9 and SOX5 to AF remodeling expressed as PR interval prolongation, low voltage areas and left atrial dilatation and subsequently to response to catheter ablation. Future and larger studies are necessary to replicate and apply these findings with the aim of designing AF pathophysiology-based multi-locus risk scores.

Susceptibility to Childhood Pneumonia: A Genome-Wide Analysis

Previous studies have indicated that in adult smokers, a history of childhood pneumonia is associated with reduced lung function and chronic obstructive pulmonary disease. There have been few previous investigations using genome-wide association studies to investigate genetic predisposition to pneumonia. This study aims to identify the genetic variants associated with the development of pneumonia during childhood and over the course of the lifetime. Study subjects included current and former smokers with and without chronic obstructive pulmonary disease participating in the COPDGene Study. Pneumonia was defined by subject self-report, with childhood pneumonia categorized as having the first episode at <16 years. Genome-wide association studies for childhood pneumonia (843 cases, 9,091 control subjects) and lifetime pneumonia (3,766 cases, 5,659 control subjects) were performed separately in non-Hispanic whites and African Americans. Non-Hispanic white and African American populations were combined in the meta-analysis. Top genetic variants from childhood pneumonia were assessed in network analysis. No single-nucleotide polymorphisms reached genome-wide significance, although we identified potential regions of interest. In the childhood pneumonia analysis, this included variants in NGR1 (P = 6.3 × 10^-8), PAK6 (P = 3.3 × 10^-7), and near MATN1 (P = 2.8 × 10^-7). In the lifetime pneumonia analysis, this included variants in LOC339862 (P = 8.7 × 10^-7), RAPGEF2 (P = 8.4 × 10^-7), PHACTR1 (P = 6.1 × 10^-7), near PRR27 (P = 4.3 × 10^-7), and near MCPH1 (P = 2.7 × 10^-7). Network analysis of the genes associated with childhood pneumonia included top networks related to development, blood vessel morphogenesis, muscle contraction, WNT signaling, DNA damage, apoptosis, inflammation, and immune response (P ≤ 0.05). We have identified genes potentially associated with the risk of pneumonia. Further research will be required to confirm these associations and to determine biological mechanisms.

CLINICAL TRIAL REGISTRATION

NCT00608764.

Polymorphic Variations Associated With Doxorubicin-Induced Cardiotoxicity in Breast Cancer Patients

Doxorubicin (DOX) is a commonly used antineoplastic agent for the treatment of various malignancies, and its use is associated with unpredictable cardiotoxicity. Susceptibility to DOX cardiotoxicity is largely patient dependent, suggesting genetic predisposition. We have previously found that individual sensitivity to DOX cardiotoxicity was associated with differential expression of genes implicated in inflammatory response and immune trafficking, which was consistent with the increasing number of reports highlighting the important role of human leukocyte antigen (HLA) complex polymorphism in hypersensitivity to drug toxicity. This pilot study aimed to investigate DNA from patients treated with DOX-based chemotherapy for breast cancer and to correlate the results with the risk for DOX-associated cardiotoxicity. We have identified 18 SNPs in nine genes in the HLA region (NFKBIL1, TNF-α, ATP6V1G2-DDX39B, MSH5, MICA, LTA, BAT1, and NOTCH4) and in the psoriasis susceptibility region of HLA-C as potential candidates for association with DOX cardiotoxicity. These results, albeit preliminary and involving a small number of patients, are consistent with reports showing the presence of susceptibility loci within the HLA gene region for several inflammatory and autoimmune diseases, and with our previous findings indicating that the increased sensitivity to DOX cardiotoxicity was associated with dysregulation of genes implicated both in inflammation and autoimmune disorders.

Enrichment of putative PAX8 target genes at serous epithelial ovarian cancer susceptibility loci

BACKGROUND

Genome-wide association studies (GWAS) have identified 18 loci associated with serous ovarian cancer (SOC) susceptibility but the biological mechanisms driving these findings remain poorly characterised. Germline cancer risk loci may be enriched for target genes of transcription factors (TFs) critical to somatic tumorigenesis.

METHODS

All 615 TF-target sets from the Molecular Signatures Database were evaluated using gene set enrichment analysis (GSEA) and three GWAS for SOC risk: discovery (2196 cases/4396 controls), replication (7035 cases/21 693 controls; independent from discovery), and combined (9627 cases/30 845 controls; including additional individuals).

RESULTS

The PAX8-target gene set was ranked 1/615 in the discovery (PGSEA<0.001; FDR=0.21), 7/615 in the replication (PGSEA=0.004; FDR=0.37), and 1/615 in the combined (PGSEA<0.001; FDR=0.21) studies. Adding other genes reported to interact with PAX8 in the literature to the PAX8-target set and applying an alternative to GSEA, interval enrichment, further confirmed this association (P=0.006). Fifteen of the 157 genes from this expanded PAX8 pathway were near eight loci associated with SOC risk at P<10-5 (including six with P<5 × 10-8). The pathway was also associated with differential gene expression after shRNA-mediated silencing of PAX8 in HeyA8 (PGSEA=0.025) and IGROV1 (PGSEA=0.004) SOC cells and several PAX8 targets near SOC risk loci demonstrated in vitro transcriptomic perturbation.

CONCLUSIONS

Putative PAX8 target genes are enriched for common SOC risk variants. This finding from our agnostic evaluation is of particular interest given that PAX8 is well-established as a specific marker for the cell of origin of SOC.

Multiple linear combination (MLC) regression tests for common variants adapted to linkage disequilibrium structure

By jointly analyzing multiple variants within a gene, instead of one at a time, gene‐based multiple regression can improve power, robustness, and interpretation in genetic association analysis. We investigate multiple linear combination (MLC) test statistics for analysis of common variants under realistic trait models with linkage disequilibrium (LD) based on HapMap Asian haplotypes. MLC is a directional test that exploits LD structure in a gene to construct clusters of closely correlated variants recoded such that the majority of pairwise correlations are positive. It combines variant effects within the same cluster linearly, and aggregates cluster‐specific effects in a quadratic sum of squares and cross‐products, producing a test statistic with reduced degrees of freedom (df) equal to the number of clusters. By simulation studies of 1000 genes from across the genome, we demonstrate that MLC is a well‐powered and robust choice among existing methods across a broad range of gene structures. Compared to minimum P‐value, variance‐component, and principal‐component methods, the mean power of MLC is never much lower than that of other methods, and can be higher, particularly with multiple causal variants. Moreover, the variation in gene‐specific MLC test size and power across 1000 genes is less than that of other methods, suggesting it is a complementary approach for discovery in genome‐wide analysis. The cluster construction of the MLC test statistics helps reveal within‐gene LD structure, allowing interpretation of clustered variants as haplotypic effects, while multiple regression helps to distinguish direct and indirect associations.

Computational discovery of transcription factors associated with drug response

This study integrates gene expression, genotype and drug response data in lymphoblastoid cell lines with transcription factor (TF)-binding sites from ENCODE (Encyclopedia of Genomic Elements) in a novel methodology that elucidates regulatory contexts associated with cytotoxicity. The method, GENMi (Gene Expression iN the Middle), postulates that single-nucleotide polymorphisms within TF-binding sites putatively modulate its regulatory activity, and the resulting variation in gene expression leads to variation in drug response. Analysis of 161 TFs and 24 treatments revealed 334 significantly associated TF-treatment pairs. Investigation of 20 selected pairs yielded literature support for 13 of these associations, often from studies where perturbation of the TF expression changes drug response. Experimental validation of significant GENMi associations in taxanes and anthracyclines across two triple-negative breast cancer cell lines corroborates our findings. The method is shown to be more sensitive than an alternative, genome-wide association study-based approach that does not use gene expression. These results demonstrate the utility of GENMi in identifying TFs that influence drug response and provide a number of candidates for further testing.

Gene-based analysis identified the gene ZNF248 is associated with late-onset asthma in African Americans

BACKGROUND

Late-onset asthma (LOA) has distinct characteristics and its pathogenesis might rely on unique pathways. Although current studies are focused primarily on childhood asthma, more research is needed to show the mechanisms underlying LOA.

OBJECTIVE

To conduct genomewide association analysis and gene-based analysis to identify single-nucleotide polymorphisms and genes associated with LOA.

METHODS

The Women's Health Initiative (WHI) observational cohort and the Multi-Ethnic Study of Atherosclerosis (MESA) were used to identify subjects with LOA. The association between LOA and body mass index and smoking was evaluated. In the discovery stage of the genetic analysis, 1,218 African American subjects from WHI with genotype data (271 cases and 947 controls) were used for single-nucleotide polymorphism and gene-based association analyses. Significant or suggestive results were subsequently investigated in an independent African American population from MESA (38 cases and 806 controls).

RESULTS

In WHI, the relative odds for LOA in obese vs normal-weight subjects was 2.55 (95% confidence interval 1.74-3.76). Ever smokers also had greater odds for LOA compared with never smokers (odds ratio 1.59, 95% confidence interval 1.21-2.09). The same trends were observed in MESA. In WHI, 6 single-nucleotide polymorphisms were associated with LOA at a genomewide-suggestive significance level (P < 1.0 × 10(-5)). The gene ZNF248 was associated with LOA and reached genomewide significance (P = 4.0 × 10(-7)). In MESA, the association between ZNF248 and LOA was successfully replicated (P = .015).

CONCLUSION

Smoking and obesity are risk factors for LOA. ZNF248 confers increased susceptibility to LOA in African Americans.

Adaptive gene- and pathway-trait association testing with GWAS summary statistics

BACKGROUND

Gene- and pathway-based analyses offer a useful alternative and complement to the usual single SNP-based analysis for GWAS. On the other hand, most existing gene- and pathway-based tests are not highly adaptive, and/or require the availability of individual-level genotype and phenotype data. It would be desirable to have highly adaptive tests applicable to summary statistics for single SNPs. This has become increasingly important given the popularity of large-scale meta-analyses of multiple GWASs and the practical availability of either single GWAS or meta-analyzed GWAS summary statistics for single SNPs.

RESULTS

We extend two adaptive tests for gene- and pathway-level association with a univariate trait to the case with GWAS summary statistics without individual-level genotype and phenotype data. We use the WTCCC GWAS data to evaluate and compare the proposed methods and several existing methods. We further illustrate their applications to a meta-analyzed dataset to identify genes and pathways associated with blood pressure, demonstrating the potential usefulness of the proposed methods. The methods are implemented in R package aSPU, freely and publicly available.

AVAILABILITY AND IMPLEMENTATION

https://cran.r-project.org/web/packages/aSPU/ CONTACT: weip@biostat.umn.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Gene-based meta-analysis of genome-wide association studies implicates new loci involved in obesity

To date, genome-wide association studies (GWASs) have identified >100 loci with single variants associated with body mass index (BMI). This approach may miss loci with high allelic heterogeneity; therefore, the aim of the present study was to use gene-based meta-analysis to identify regions with high allelic heterogeneity to discover additional obesity susceptibility loci. We included GWAS data from 123 865 individuals of European descent from 46 cohorts in Stage 1 and Metabochip data from additional 103 046 individuals from 43 cohorts in Stage 2, all within the Genetic Investigation of ANthropometric Traits (GIANT) consortium. Each cohort was tested for association between ∼2.4 million (Stage 1) or ∼200 000 (Stage 2) imputed or genotyped single variants and BMI, and summary statistics were subsequently meta-analyzed in 17 941 genes. We used the 'VErsatile Gene-based Association Study' (VEGAS) approach to assign variants to genes and to calculate gene-based P-values based on simulations. The VEGAS method was applied to each cohort separately before a gene-based meta-analysis was performed. In Stage 1, two known (FTO and TMEM18) and six novel (PEX2, MTFR2, SSFA2, IARS2, CEP295 and TXNDC12) loci were associated with BMI (P < 2.8 × 10(-6) for 17 941 gene tests). We confirmed all loci, and six of them were gene-wide significant in Stage 2 alone. We provide biological support for the loci by pathway, expression and methylation analyses. Our results indicate that gene-based meta-analysis of GWAS provides a useful strategy to find loci of interest that were not identified in standard single-marker analyses due to high allelic heterogeneity.

Using Hamming Distance as Information for SNP-Sets Clustering and Testing in Disease Association Studies

The availability of high-throughput genomic data has led to several challenges in recent genetic association studies, including the large number of genetic variants that must be considered and the computational complexity in statistical analyses. Tackling these problems with a marker-set study such as SNP-set analysis can be an efficient solution. To construct SNP-sets, we first propose a clustering algorithm, which employs Hamming distance to measure the similarity between strings of SNP genotypes and evaluates whether the given SNPs or SNP-sets should be clustered. A dendrogram can then be constructed based on such distance measure, and the number of clusters can be determined. With the resulting SNP-sets, we next develop an association test HDAT to examine susceptibility to the disease of interest. This proposed test assesses, based on Hamming distance, whether the similarity between a diseased and a normal individual differs from the similarity between two individuals of the same disease status. In our proposed methodology, only genotype information is needed. No inference of haplotypes is required, and SNPs under consideration do not need to locate in nearby regions. The proposed clustering algorithm and association test are illustrated with applications and simulation studies. As compared with other existing methods, the clustering algorithm is faster and better at identifying sets containing SNPs exerting a similar effect. In addition, the simulation studies demonstrated that the proposed test works well for SNP-sets containing a large proportion of neutral SNPs. Furthermore, employing the clustering algorithm before testing a large set of data improves the knowledge in confining the genetic regions for susceptible genetic markers.

A genome-wide screening and SNPs-to-genes approach to identify novel genetic risk factors associated with frontotemporal dementia

Frontotemporal dementia (FTD) is the second most prevalent form of early onset dementia after Alzheimer's disease (AD). We performed a case-control association study in an Italian FTD cohort (n = 530) followed by the novel single nucleotide polymorphisms (SNPs)-to-genes approach and functional annotation analysis. We identified 2 novel potential loci for FTD. Suggestive SNPs reached p-values ∼10⁻⁷ and odds ratio > 2.5 (2p16.3) and 1.5 (17q25.3). Suggestive alleles at 17q25.3 identified a disease-associated haplotype causing decreased expression of –cis genes such as RFNG and AATK involved in neuronal genesis and differentiation and axon outgrowth, respectively. We replicated this locus through the SNPs-to-genes approach. Our functional annotation analysis indicated significant enrichment for functions of the brain (neuronal genesis, differentiation, and maturation), the synapse (neurotransmission and synapse plasticity), and elements of the immune system, the latter supporting our recent international FTD–genome-wide association study. This is the largest genome-wide study in Italian FTD to date. Although our results are not conclusive, we set the basis for future replication studies and identification of susceptible molecular mechanisms involved in FTD pathogenesis.

Proteomic and genomic evidence implicates the postsynaptic density in schizophrenia

The postsynaptic density (PSD) contains a complex set of proteins of known relevance to neuropsychiatric disorders, and schizophrenia specifically. We enriched for this anatomical structure, in the anterior cingulate cortex, of 20 schizophrenia samples and 20 controls from the Stanley Medical Research Institute, and used unbiased shotgun proteomics incorporating label-free quantitation to identify differentially expressed proteins. Quantitative investigation of the PSD revealed more than 700 protein identifications and 143 differentially expressed proteins. Prominent among these were altered expression of proteins involved in clathrin-mediated endocytosis (CME) (Dynamin-1, adaptor protein 2) and N-methyl-D-aspartate (NMDA)-interacting proteins such as CYFIP2, SYNPO, SHANK3, ESYT and MAPK3 (all P<0.0015). Pathway analysis of the differentially expressed proteins implicated the cellular processes of endocytosis, long-term potentiation and calcium signaling. Both single-gene and gene-set enrichment analyses in genome-wide association data from the largest schizophrenia sample to date of 13,689 cases and 18,226 controls show significant association of HIST1H1E and MAPK3, and enrichment of our PSD proteome. Taken together, our data provide robust evidence implicating PSD-associated proteins and genes in schizophrenia, and suggest that within the PSD, NMDA-interacting and endocytosis-related proteins contribute to disease pathophysiology.

MGAS: a powerful tool for multivariate gene-based genome-wide association analysis

Motivation: Standard genome-wide association studies, testing the association between one phenotype and a large number of single nucleotide polymorphisms (SNPs), are limited in two ways: (i) traits are often multivariate, and analysis of composite scores entails loss in statistical power and (ii) gene-based analyses may be preferred, e.g. to decrease the multiple testing problem.

Results: Here we present a new method, multivariate gene-based association test by extended Simes procedure (MGAS), that allows gene-based testing of multivariate phenotypes in unrelated individuals. Through extensive simulation, we show that under most trait-generating genotype–phenotype models MGAS has superior statistical power to detect associated genes compared with gene-based analyses of univariate phenotypic composite scores (i.e. GATES, multiple regression), and multivariate analysis of variance (MANOVA). Re-analysis of metabolic data revealed 32 False Discovery Rate controlled genome-wide significant genes, and 12 regions harboring multiple genes; of these 44 regions, 30 were not reported in the original analysis.

Conclusion: MGAS allows researchers to conduct their multivariate gene-based analyses efficiently, and without the loss of power that is often associated with an incorrectly specified genotype–phenotype models.

Availability and implementation: MGAS is freely available in KGG v3.0 (http://statgenpro.psychiatry.hku.hk/limx/kgg/download.php). Access to the metabolic dataset can be requested at dbGaP (https://dbgap.ncbi.nlm.nih.gov/). The R-simulation code is available from http://ctglab.nl/people/sophie_van_der_sluis.

Contact: mxli@hku.hk

Supplementary information: Supplementary data are available at Bioinformatics online.

Pathway analysis approaches for rare and common variants: insights from Genetic Analysis Workshop 18

Pathway analysis, broadly defined as a group of methods incorporating a priori biological information from public databases, has emerged as a promising approach for analyzing high-dimensional genomic data. As part of Genetic Analysis Workshop 18, seven research groups applied pathway analysis techniques to whole-genome sequence data from the San Antonio Family Study. Overall, the groups found that the potential of pathway analysis to improve detection of causal variants by lowering the multiple-testing burden and incorporating biologic insight remains largely unrealized. Specifically, there is a lack of best practices at each stage of the pathway approach: annotation, analysis, interpretation, and follow-up. Annotation of genetic variants is inconsistent across databases, incomplete, and biased toward known genes. At the analysis stage insufficient statistical power remains a major challenge. Analyses combining rare and common variants may have an inflated type I error rate and may not improve detection of causal genes. Inclusion of known causal genes may not improve statistical power, although the fraction of explained phenotypic variance may be a more appropriate metric. Interpretation of findings is further complicated by evidence in support of interactions between pathways and by the lack of consensus on how to best incorporate functional information. Finally, all presented approaches warranted follow-up studies, both to reduce the likelihood of false-positive findings and to identify specific causal variants within a given pathway. Despite the initial promise of pathway analysis for modeling biological complexity of disease phenotypes, many methodological challenges currently remain to be addressed.

Application of family-based tests of association for rare variants to pathways

Pathway analysis approaches for sequence data typically either operate in a single stage (all variants within all genes in the pathway are combined into a single, very large set of variants that can then be analyzed using standard "gene-based" test statistics) or in 2-stages (gene-based p values are computed for all genes in the pathway, and then the gene-based p values are combined into a single pathway p value). To date, little consideration has been given to the performance of gene-based tests (typically designed for a smaller number of single-nucleotide variants [SNVs]) when the number of SNVs in the gene or in the pathway is very large and the genotypes come from sequence data organized in large pedigrees. We consider recently proposed gene-based tests for rare variants from complex pedigrees that test for association between a large set of SNVs and a qualitative phenotype of interest (1-stage analyses) as well as 2-stage approaches. We find that many of these methods show inflated type I errors when the number of SNVs in the gene or the pathway is large (>200 SNVs) and when using standard approaches to estimate the genotype covariance matrix. Alternative methods are needed when testing very large sets of SNVs in 1-stage approaches.

Efficient haplotype block recognition of very long and dense genetic sequences

Background

The new sequencing technologies enable to scan very long and dense genetic sequences, obtaining datasets of genetic markers that are an order of magnitude larger than previously available. Such genetic sequences are characterized by common alleles interspersed with multiple rarer alleles. This situation has renewed the interest for the identification of haplotypes carrying the rare risk alleles. However, large scale explorations of the linkage-disequilibrium (LD) pattern to identify haplotype blocks are not easy to perform, because traditional algorithms have at least Θ(n²) time and memory complexity.

Results

We derived three incremental optimizations of the widely used haplotype block recognition algorithm proposed by Gabriel et al. in 2002. Our most efficient solution, called MIG ⁺⁺, has only Θ(n) memory complexity and, on a genome-wide scale, it omits >80% of the calculations, which makes it an order of magnitude faster than the original algorithm. Differently from the existing software, the MIG ⁺⁺ analyzes the LD between SNPs at any distance, avoiding restrictions on the maximal block length. The haplotype block partition of the entire HapMap II CEPH dataset was obtained in 457 hours. By replacing the standard likelihood-based D^′ variance estimator with an approximated estimator, the runtime was further improved. While producing a coarser partition, the approximate method allowed to obtain the full-genome haplotype block partition of the entire 1000 Genomes Project CEPH dataset in 44 hours, with no restrictions on allele frequency or long-range correlations. These experiments showed that LD-based haplotype blocks can span more than one million base-pairs in both HapMap II and 1000 Genomes datasets. An application to the North American Rheumatoid Arthritis Consortium (NARAC) dataset shows how the MIG ⁺⁺ can support genome-wide haplotype association studies.

Conclusions

The MIG ⁺⁺ enables to perform LD-based haplotype block recognition on genetic sequences of any length and density. In the new generation sequencing era, this can help identify haplotypes that carry rare variants of interest. The low computational requirements open the possibility to include the haplotype block structure into genome-wide association scans, downstream analyses, and visual interfaces for online genome browsers.

Incorporating prior knowledge to increase the power of genome-wide association studies

Typical methods of analyzing genome-wide single nucleotide variant (SNV) data in cases and controls involve testing each variant's genotypes separately for phenotype association, and then using a substantial multiple-testing penalty to minimize the rate of false positives. This approach, however, can result in low power for modestly associated SNVs. Furthermore, simply looking at the most associated SNVs may not directly yield biological insights about disease etiology. SNVset methods attempt to address both limitations of the traditional approach by testing biologically meaningful sets of SNVs (e.g., genes or pathways). The number of tests run in a SNVset analysis is typically much lower (hundreds or thousands instead of millions) than in a traditional analysis, so the false-positive rate is lower. Additionally, by testing SNVsets that are biologically meaningful finding a significant set may more quickly yield insights into disease etiology.In this chapter we summarize the short history of SNVset testing and provide an overview of the many recently proposed methods. Furthermore, we provide detailed step-by-step instructions on how to perform a SNVset analysis, including a substantial number of practical tips and questions that researchers should consider before undertaking a SNVset analysis. Lastly, we describe a companion R package (snvset) that implements recently proposed SNVset methods. While SNVset testing is a new approach, with many new methods still being developed and many open questions, the promise of the approach is worth serious consideration when considering analytic methods for GWAS.